【摘要】 单基线PolInSAR受观测信息不足影响常假设体散射占优极化方式地体幅度比为零，以实现植被高参数的反演，该假设导致植被高参数反演存在较大偏差。利用多基线观测数据，可补充观测信息实现地体幅度比参数的反演。然而，多基线反演模型引入过多模型参数，造成部分参数存在近似线性相关性，引起反演模型病态，常规算法难以得到准确的模型参数估值。鉴于此，本文提出了植被高反演多基线分步解算方法。首先，基于多基线体散射占优极化数据构建多基线反演函数模型，利用正则化方法处理模型病态问题，获得模型参数正则化估值，并通过均方误差分析方法筛选模型参数估值，确定出可靠的地体幅度比参数估值；然后，利用地体幅度比参数估值重估多基线纯体相干性，并基于最小二范估计准则融合多基线纯体相干性反演植被高参数。试验结果表明，本文方法可有效提高植被高反演精度，相较于三阶段算法与多基线常规解法，反演精度提高了26%,具备较好的可行性和有效性。更多还原

【Abstract】 Affected by the insufficient information of single baseline observation data, the three-stage method assume the ground-to-volume ratio(GVR) to be zero so as to invert the vegetation height. However, this assumption introduces much biases into the parameter estimates which greatly limit the accuracy of the vegetation height inversion. Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR. Nevertheless, there are many geometry parameters that share similar values in a multi-baseline model, which lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm. To this end, we propose a new step-by-step inversion method applied to the multi-baseline observations. Firstly, an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data, and the regularized estimates of model parameters are obtained by regularization method. Then, the reliable estimates of GVR is determined by the mean square error(MSE) analysis of each regularized parameter estimation. Secondly, using the estimated GVR extracts the pure volume coherence, and then inverting the vegetation heights from the pure volume coherences by least squares estimation. The experimental results show that the new method can improve the vegetation height inversion result effectively. The inversion accuracy is improved by 26% with respect to three-stage method and the conventional solution of multi-baseline. The results have demonstrated the feasibility and effectiveness of the new method.更多还原